What is Decision Automation? Automation of the Decision-Making Process Explained
Bots & People
5 min read
The goal of any business is to quickly make good decisions that increase profitability, improve efficiency and reduce costs. To do this, companies have to process an astonishing amount of data. Evaluating and classifying this extraordinary amount of data is a real Herculean task, even for experienced managers, and hardly allows for quick decisions. The solution to this problem is called Decision Automation (DA). Decision Automation opens up a way for companies to optimize decision making by using rules and data to automatically make accurate, effective and fact-based decisions.
What is Decision Automation?
A decision is not just a choice between defined alternative actions, but encompasses the entire process of collecting and evaluating information about a situation, determining a need for a decision, identifying relevant options for action, and selecting the optimal action. Therefore, decision automation systems are also suitable for the complete or partial automation of decision processes. In Decision Automation (DA), in simple terms, a computer program using data, rules and criteria, rather than a human, makes the decision. DA uses artificial intelligence, data, and business rules to help companies automate decision-making processes in a variety of areas. Decision automation is typically applied to routine and repetitive decisions that are part of an organization's daily actions. These types of decisions are referred to as operational, meaning that they drive the day-to-day operations of a particular business. Ideally, decision automation is a fully automated process, but with some room for human input. Generally, however, with DA a human decision maker is no longer necessary in a given decision situation.
Stages of Decision Automation
Decision models such as the observe-orient-decide-act (OODA) loop are available to identify specific decision-making activities. This model is also known as the "Boyd Cycle" after its creator and military strategist, Air Force Colonel John Boyd. By using the OODA loop, the different levels of decision automation - full decision automation, advanced decision support, and decision support - can be further characterized by considering the degree of automation of each decision-making activity.
Complete decision automation
The complete automation of decisions is based on prescriptive analyses and, if necessary, also on predictive analyses. Decision making is the sole responsibility of the system. Benefits include speed, scalability and consistency of decision making, leveraging the growing availability of contextual data and artificial intelligence (AI) technologies. In decision automation, the activities of observing, orienting, and deciding are fully automated. The activities are often semi-automated, as the application of decision automation triggers other systems or notifies actors to implement the decision by executing an action. In cases where the action is also fully automated, the system has become "autonomous."
Advanced decision support
Augmented decision support is semi-automated decision making where humans make the final decision and have a choice between multiple decision alternatives. Augmented decision support, also referred to as augmented decision support, creates synergies between human knowledge and common sense on the one hand and digital technologies that can handle more data and greater complexity on the other. In augmented decision support, observation and orientation are automated, while decision-making is semi-automated, using prescriptive analytics. The system offers suggestions to the user who makes the final decision.
Decision support accompanies human decisions only through descriptive, diagnostic or predictive analysis. In decision support, only observing is fully automated in terms of data collection. Orienting is semi-automated, providing insights to users who still need to draw their own conclusions before they can decide and act.
Advantages of automated decision making
Decision Automation has several advantages in store for companies. One of the most significant benefits is the ability to make fast and error-free decisions. Automation is applied to business decisions around the clock, enabling consistency across all decisions that cannot be achieved by an individual or a group of decision makers. By explicitly modeling decisions, there is always clarity about how decisions are made. At the same time, compliance is improved. By reducing the occurrence of regulatory or contractual errors, organizations can save themselves from fines for non-compliance.
Risk avoidance for discretionary decisions
Decision Automation increases productivity and reduces risks and errors in the decisions made. Decision Automation helps organizations make better decisions because, for example, customer-centric decisions are based on calculations, data, and domain expertise and knowledge. At the same time, Decision Automation eliminates the risk of decisions based on individual discretion. DA minimizes the time between when an event occurs and when it is acted upon, and increases the accuracy of the decision by using as much relevant data as possible, in real time if necessary, to create a more complete and up-to-date situational awareness. This awareness, in turn, is used as the basis for applying knowledge, experience, decision logic, models, and algorithms to improve the accuracy of decisions.
How does Decision Automation work?
Simple or moderately complex decisions can be efficiently implemented by Citizen Developers using low code development tools. These decision automation tools digitally map decision trees. For each branch, a condition and an associated action can be defined. Decision paths and logics can thus be combined with specific actions such as e-mail dispatch, document generation or integrations with other applications. The automated decision workflows can be embedded on web pages or intranets, or run invisibly in the background. For example, it can be an interactive questionnaire that uses different text modules depending on the answers and delivers a result or starts an action at the end. Other examples include legal audits, risk analyses or insurance claims processing. Companies can use such tools to digitize and scale knowledge that is necessary for decision-making.
Rules or algorithms?
Decisions can be made through knowledge of business rules (rule-based decisions) or based on data and information (data-driven decisions) with the help of quantitative, logical, heuristic, statistical, predictive, and/or artificial intelligence (AI)-based algorithms. An algorithm specifies one or more actions to be applied to the situation. Ideally, both techniques work hand-in-hand to automate a decision. Rule-based decision automation is useful when the explainability and justification of certain decisions are important for the customer or client, for authorities or third parties. Data-driven decision automation works particularly well when predictive models are needed or there is uncertainty about how a situation will develop.
The reinsurance human
Intelligent decision automation programs learn from successes and failures and automatically improve and update stored procedures, rules or probabilities. Human decision makers simply determine the alternatives, rules, models and methods used to make decisions. In any case, however, Decision Automation should also provide the option that in scenarios where fully automated decisions are not possible due to ambiguities, uncertainty, etc. regarding the decisions, domain experts can intervene and provide appropriate input.
Application Examples of Decision Automation
In many industries such as insurance, finance, banking, etc., routine and redress decisions are automated to ensure that quality and consistency are not compromised. Especially when decisions need to be made quickly, the risk of a wrong decision can be considered low, and decisions/results are "reversible", automating the decision through algorithms, Artificial Intelligence (AI) or Machine Learning (ML) makes practical and economic sense. In insurance, for example, many of the decisions made in underwriting, claims processing, pricing, discounting, etc. can be automated using business rules automation. Decision automation can be used in an air traffic control environment, flexible manufacturing systems, petroleum refining, high-speed sorting systems, control decision automation, intelligent monitoring and decision making in intensive situations, airborne collision avoidance systems, building automation systems, facility management systems, and laboratory management systems. Question catalogs can determine whether and how to introduce short-time work. Airlines can use automated decision applications to set prices based on seat availability and the hour or day of purchase.
Decision Automation is more than just a trend and more than a concept - it is deeply rooted in business and back-end automation and is increasingly used in all industries. Many DA tools offer an intuitive interface that can be used to automate decision making using drag and drop. The user-friendly handling may make some people doubt the power of the tools. But this is not the case. DA tools are quite capable of mastering complex decision-making processes and demanding use cases. For example, employees of law firms, banks or consulting companies can automate decision-making processes themselves. Complex decision-making knowledge, just like any other knowledge in the company, becomes a scalable resource through Decision Automation, which is available everywhere and enables fast, efficient decisions.
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